The focus of on-going projects is the use of tractable genetic models to mechanistically investigate therapeutic vulnerabilities of CRPC. Historically, the ability to grow prostate cancer in culture has been extremely limited, leading to a lack of in vitro models that recapitulate the diversity of human prostate cancer. Therefore, although extensive genomic characterization exists for primary and metastatic prostate cancer patient samples, the ability to interrogate mutational mechanisms in the naturally-occurring genomic context has been severely limited. Also, the dearth of models has impeded interrogating the generality of many interesting and potentially translatable findings from studies using individual prostate cancer models. Mechanistic understanding is key to defining highly clinically relevant information such as predictive mutations, novel therapeutic combinations, and diagnostic or predictive biomarkers. Recent advances in growing metastatic prostate cancer biopsies from the Clevers/Sawyers labs have used methodology adapted from conditions developed for the growth of intestinal stem cells and human colon cancer, often referred to as organoid growth conditions. The success reported by Sawyers and colleagues for the establishment of cultures from metastatic prostate cancer biopsies is 20%. This low success rate is a significant impediment to biobank establishment due to the relatively infrequent biopsy rate for this patient population. However, metastatic castrate-resistant prostate cancer (mCRPC) patient-derived xenografts (PDXs) recapitulate the genetic and phenotypic diversity of the disease. We have invested significant effort to set up the infrastructure and adapt the University of Washington mCRPC PDX collection (so-called LuCaP models) to growth in organoid culture. Genetically characterized PDX cohorts in other solid cancers have been useful and predictive of clinical outcome. The LuCaP models are particularly valuable in representing a range of genotypes and phenotypes of acquired resistance to ADT. Although LuCaP models were derived from CRPC patients, the PDXs demonstrate variable sensitivity to growth in castrated hosts. This likely represents differences in the hormone physiology and microenvironment of the mouse in comparison to human and also suggests that resistance to ADT is a continuum. AR is not a typical oncogene since it drives multiple pathways that encompass growth, differentiation, and lineage selection that may be plastic and differentially expressed in the tumor population. The full utility of mCRPC PDX models for high throughput and mechanistic analyses has not been realized due to their lack of robust growth in vitro. Using 23 representatives from the LuCaP mCRPC cohort, we developed optimized organoid conditions that allow growth in a majority of PDX models, and also improved establishment of CRPC patient biopsies over current methods. Adenocarcinoma organoids demonstrated continued dependence on AR signaling, replicating a dominant characteristic of CRPC. Unexpectedly, we found that p38 MAPK activity was frequently essential and led to increased AKT activity. GSK3-beta inactivation was one functional AKT target, although not entirely sufficient to replace p38 activity. Genomic analyses revealed considerable subclonal heterogeneity with high conservation between LuCaP PDX tumors and organoid cultures. The LuCaP PDX/organoid platform provides an expansive, manipulable platform to investigate predictive and mechanistic questions for mCRPC. There are immediate implications for prostate cancer drug screening as well as long-term potential to advance the understanding of fundamental mechanisms driving the disease and resistance to therapy, previously not possible due to insufficient numbers of relevant models. In addition, we have succeeded in establishing a small number of mCRPC biopsy-derived organoid cultures. These cultures allow us to apply principles of precision medicine, including thorough genomic characterization and drug sensitivity screening, in order to discover efficacious treatment options defined by genomic biomarkers.